基于颜色空间转换的颜色复原方法研究

Colour Recovery Method Based on Color Space Transformation

中文关键词: 颜色复原  颜色空间转换  线性回归  BP神经网络

英文关键词: color recovery  color space transformation  linear regression  BP neural network

中文摘要:

      目的 研究解决因成像原理、 元件性能、 机械上的限制等因素导致的色彩失真与偏差的方法。方法 通过对基于BP神经网络的颜色复原和基于全局多项式回归的颜色复原等2种方法进行对比研究， 提出基于色调分区多项式回归的、 由 RGB 到 L*a*b*的颜色复原转换方法。结果 基于 BP 神经网络的颜色复原得到的最小色差为 2.8476， 基于全局多项式回归的颜色复原得到的最小色差为2.857， 二者相差仅 0.3%； 而经过分区后的多项式回归颜色复原得到的平均色差为 2.206， 比基于 BP神经网络和全局多项式回归方法降低了 23%左右的色差。结论 经过分区后的多项式回归颜色复原方法能更有效地提高颜色复原的精度。

英文摘要:

      This paper studied the methods dealing with the distortions and deviations in color which are caused by the factors such as the limitations on imaging-forming principle, device performance and machining controls This paper compared color restoration by the BP neural network versus the global polynomial regression. Then this paper presented an RGB to L*a*b* transformation method based on polynomial regression of each subspace through dividing the space into sub-domains in accordance with the hue. The calculated average color difference based on BP neural network was 2.8476, and the difference based on global polynomial regression was 2.857; the two had only 0.3% difference. However, after using polynomial regression of each subspace to recover the colors, the average color difference was 2.206, reduced by 23% compared with the above two methods. Recovering the colors by using polynomial regression of each subspace can effectively improve the precision of color restoration.